Fig. 2

Feature selection by using a parametric method, the least absolute shrinkage and selection operator (LASSO) regression. a Significant fibrosis feature selection of tuning parameter (λ) in the LASSO model used 10-fold cross-validation via minimum criteria. The AUC curve was plotted versus log(λ). Dotted vertical lines were drawn at the optimal values by using the minimum criteria and the 1 standard error of the minimum criteria (the 1 – standard error criteria). The optimal log(λ) of − 3.96 was chosen. b Cirrhosis feature selection and the optimal log(λ) of − 4.83 was chosen. c LASSO coefficient profiles of the 18 initially selected features. A vertical line was plotted at the optimal λ value, which resulted in 9 features with nonzero coefficients. d LASSO coefficient profiles of the 16 initially selected features. A vertical line was plotted at the optimal λ value, which resulted in 9 features with nonzero coefficients